We demonstrate that, for a chemical reaction network (CRN) engaged in energy transduction, its optimal operation from a thermodynamic efficiency standpoint is contingent upon its working conditions. Analogously to the bicycle gear system, CRNs have at their disposal several transducing mechanisms characterized by different yields. We highlight the critical role of the CRN's elementary flux modes in determining this "gearing" and their impact on maximizing energy transduction efficiency. Furthermore, we introduce an enzymatically regulated CRN, engineered to autonomously adjust its "gear", thereby optimizing its efficiency under different external conditions.
{"title":"Elementary Flux Modes as CRN Gears for Free Energy Transduction","authors":"Massimo Bilancioni, Massimiliano Esposito","doi":"arxiv-2405.17960","DOIUrl":"https://doi.org/arxiv-2405.17960","url":null,"abstract":"We demonstrate that, for a chemical reaction network (CRN) engaged in energy\u0000transduction, its optimal operation from a thermodynamic efficiency standpoint\u0000is contingent upon its working conditions. Analogously to the bicycle gear\u0000system, CRNs have at their disposal several transducing mechanisms\u0000characterized by different yields. We highlight the critical role of the CRN's\u0000elementary flux modes in determining this \"gearing\" and their impact on\u0000maximizing energy transduction efficiency. Furthermore, we introduce an\u0000enzymatically regulated CRN, engineered to autonomously adjust its \"gear\",\u0000thereby optimizing its efficiency under different external conditions.","PeriodicalId":501325,"journal":{"name":"arXiv - QuanBio - Molecular Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141166761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Phosphorylation networks, representing the mechanisms by which proteins are phosphorylated at one or multiple sites, are ubiquitous in cell signalling and display rich dynamics such as unlimited multistability. Dual-site phosphorylation networks are known to exhibit oscillations in the form of periodic trajectories, when phosphorylation and dephosphorylation occurs as a mixed mechanism: phosphorylation of the two sites requires one encounter of the kinase, while dephosphorylation of the two sites requires two encounters with the phosphatase. A still open question is whether a mechanism requiring two encounters for both phosphorylation and dephosphorylation also admits oscillations. In this work we provide evidence in favor of the absence of oscillations of this network by precluding Hopf bifurcations in any reduced network comprising three out of its four intermediate protein complexes. Our argument relies on a novel network reduction step that preserves the absence of Hopf bifurcations, and on a detailed analysis of the semi-algebraic conditions precluding Hopf bifurcations obtained from Hurwitz determinants of the characteristic polynomial of the Jacobian of the system. We conjecture that the removal of certain reverse reactions appearing in Michaelis-Menten-type mechanisms does not have an impact on the presence or absence of Hopf bifurcations. We prove an implication of the conjecture under certain favorable scenarios and support the conjecture with additional example-based evidence.
{"title":"Network reduction and absence of Hopf Bifurcations in dual phosphorylation networks with three Intermediates","authors":"Elisenda Feliu, Nidhi Kaihnsa","doi":"arxiv-2405.16179","DOIUrl":"https://doi.org/arxiv-2405.16179","url":null,"abstract":"Phosphorylation networks, representing the mechanisms by which proteins are\u0000phosphorylated at one or multiple sites, are ubiquitous in cell signalling and\u0000display rich dynamics such as unlimited multistability. Dual-site\u0000phosphorylation networks are known to exhibit oscillations in the form of\u0000periodic trajectories, when phosphorylation and dephosphorylation occurs as a\u0000mixed mechanism: phosphorylation of the two sites requires one encounter of the\u0000kinase, while dephosphorylation of the two sites requires two encounters with\u0000the phosphatase. A still open question is whether a mechanism requiring two\u0000encounters for both phosphorylation and dephosphorylation also admits\u0000oscillations. In this work we provide evidence in favor of the absence of\u0000oscillations of this network by precluding Hopf bifurcations in any reduced\u0000network comprising three out of its four intermediate protein complexes. Our\u0000argument relies on a novel network reduction step that preserves the absence of\u0000Hopf bifurcations, and on a detailed analysis of the semi-algebraic conditions\u0000precluding Hopf bifurcations obtained from Hurwitz determinants of the\u0000characteristic polynomial of the Jacobian of the system. We conjecture that the\u0000removal of certain reverse reactions appearing in Michaelis-Menten-type\u0000mechanisms does not have an impact on the presence or absence of Hopf\u0000bifurcations. We prove an implication of the conjecture under certain favorable\u0000scenarios and support the conjecture with additional example-based evidence.","PeriodicalId":501325,"journal":{"name":"arXiv - QuanBio - Molecular Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141166601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kun Li, Xiuwen Gong, Shirui Pan, Jia Wu, Bo Du, Wenbin Hu
Drug response prediction (DRP) is a crucial phase in drug discovery, and the most important metric for its evaluation is the IC50 score. DRP results are heavily dependent on the quality of the generated molecules. Existing molecule generation methods typically employ classifier-based guidance, enabling sampling within the IC50 classification range. However, these methods fail to ensure the sampling space range's effectiveness, generating numerous ineffective molecules. Through experimental and theoretical study, we hypothesize that conditional generation based on the target IC50 score can obtain a more effective sampling space. As a result, we introduce regressor-free guidance molecule generation to ensure sampling within a more effective space and support DRP. Regressor-free guidance combines a diffusion model's score estimation with a regression controller model's gradient based on number labels. To effectively map regression labels between drugs and cell lines, we design a common-sense numerical knowledge graph that constrains the order of text representations. Experimental results on the real-world dataset for the DRP task demonstrate our method's effectiveness in drug discovery. The code is available at:https://anonymous.4open.science/r/RMCD-DBD1.
{"title":"Regressor-free Molecule Generation to Support Drug Response Prediction","authors":"Kun Li, Xiuwen Gong, Shirui Pan, Jia Wu, Bo Du, Wenbin Hu","doi":"arxiv-2405.14536","DOIUrl":"https://doi.org/arxiv-2405.14536","url":null,"abstract":"Drug response prediction (DRP) is a crucial phase in drug discovery, and the\u0000most important metric for its evaluation is the IC50 score. DRP results are\u0000heavily dependent on the quality of the generated molecules. Existing molecule\u0000generation methods typically employ classifier-based guidance, enabling\u0000sampling within the IC50 classification range. However, these methods fail to\u0000ensure the sampling space range's effectiveness, generating numerous\u0000ineffective molecules. Through experimental and theoretical study, we\u0000hypothesize that conditional generation based on the target IC50 score can\u0000obtain a more effective sampling space. As a result, we introduce\u0000regressor-free guidance molecule generation to ensure sampling within a more\u0000effective space and support DRP. Regressor-free guidance combines a diffusion\u0000model's score estimation with a regression controller model's gradient based on\u0000number labels. To effectively map regression labels between drugs and cell\u0000lines, we design a common-sense numerical knowledge graph that constrains the\u0000order of text representations. Experimental results on the real-world dataset\u0000for the DRP task demonstrate our method's effectiveness in drug discovery. The\u0000code is available at:https://anonymous.4open.science/r/RMCD-DBD1.","PeriodicalId":501325,"journal":{"name":"arXiv - QuanBio - Molecular Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141146497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The upcoming phase of space exploration not only includes trips to Mars and beyond, but also holds great promise for human progress. However, the vulnerability of space habitats to cosmic radiation, which consists of Galactic Cosmic Rays and Solar Particle Events, raises important safety concerns for astronauts and other living things that will accompany them. Research exploring the biological effects of cosmic radiation consists of experiments conducted in space itself and in simulated space environments on Earth. Notably, NASA's Space Radiation Laboratory has taken significant steps forward in simulating cosmic radiation by using particle accelerators, marking a notable advancement in this field. Intriguingly, much of the research emphasis thus far has been on understanding how cosmic radiation impacts living organisms, instead of finding ways to help them resist the radiation. In this paper, we briefly talk about current research on the biological effects of cosmic radiation and propose possible protective measures through biological interventions. In our opinion, biological pathways responsible for coping with stressors on Earth offer potential solutions for protection against the stress caused by cosmic radiation. Additionally, we recommend assessing the effectiveness of these pathways through experiments using particle accelerators to simulate the effects of cosmic radiation.
{"title":"Beyond Earthly Limits: Protection against Cosmic Radiation through Biological Response Pathways","authors":"Zahida Sultanova, Saleh Sultansoy","doi":"arxiv-2405.12151","DOIUrl":"https://doi.org/arxiv-2405.12151","url":null,"abstract":"The upcoming phase of space exploration not only includes trips to Mars and\u0000beyond, but also holds great promise for human progress. However, the\u0000vulnerability of space habitats to cosmic radiation, which consists of Galactic\u0000Cosmic Rays and Solar Particle Events, raises important safety concerns for\u0000astronauts and other living things that will accompany them. Research exploring\u0000the biological effects of cosmic radiation consists of experiments conducted in\u0000space itself and in simulated space environments on Earth. Notably, NASA's\u0000Space Radiation Laboratory has taken significant steps forward in simulating\u0000cosmic radiation by using particle accelerators, marking a notable advancement\u0000in this field. Intriguingly, much of the research emphasis thus far has been on\u0000understanding how cosmic radiation impacts living organisms, instead of finding\u0000ways to help them resist the radiation. In this paper, we briefly talk about\u0000current research on the biological effects of cosmic radiation and propose\u0000possible protective measures through biological interventions. In our opinion,\u0000biological pathways responsible for coping with stressors on Earth offer\u0000potential solutions for protection against the stress caused by cosmic\u0000radiation. Additionally, we recommend assessing the effectiveness of these\u0000pathways through experiments using particle accelerators to simulate the\u0000effects of cosmic radiation.","PeriodicalId":501325,"journal":{"name":"arXiv - QuanBio - Molecular Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141146372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Patrick Vincent N. Lubenia, Eduardo R. Mendoza, Angelyn R. Lao
Understanding the insulin signaling cascade provides insights on the underlying mechanisms of biological phenomena such as insulin resistance, diabetes, Alzheimer's disease, and cancer. For this reason, previous studies utilized chemical reaction network theory to perform comparative analyses of reaction networks of insulin signaling in healthy (INSMS: INSulin Metabolic Signaling) and diabetic cells (INRES: INsulin RESistance). This study extends these analyses using various methods which give further insights regarding insulin signaling. Using embedded networks, we discuss evidence of the presence of a structural "bifurcation" in the signaling process between INSMS and INRES. Concordance profiles of INSMS and INRES show that both have a high propensity to remain monostationary. Moreover, the concordance properties allow us to present heuristic evidence that INRES has a higher level of stability beyond its monostationarity. Finally, we discuss a new way of analyzing reaction networks through network translation. This method gives rise to three new insights: (i) each stoichiometric class of INSMS and INRES contains a unique positive equilibrium; (ii) any positive equilibrium of INSMS is exponentially stable and is a global attractor in its stoichiometric class; and (iii) any positive equilibrium of INRES is locally asymptotically stable. These results open up opportunities for collaboration with experimental biologists to understand insulin signaling better.
{"title":"Comparison of reaction networks of insulin signaling","authors":"Patrick Vincent N. Lubenia, Eduardo R. Mendoza, Angelyn R. Lao","doi":"arxiv-2405.10486","DOIUrl":"https://doi.org/arxiv-2405.10486","url":null,"abstract":"Understanding the insulin signaling cascade provides insights on the\u0000underlying mechanisms of biological phenomena such as insulin resistance,\u0000diabetes, Alzheimer's disease, and cancer. For this reason, previous studies\u0000utilized chemical reaction network theory to perform comparative analyses of\u0000reaction networks of insulin signaling in healthy (INSMS: INSulin Metabolic\u0000Signaling) and diabetic cells (INRES: INsulin RESistance). This study extends\u0000these analyses using various methods which give further insights regarding\u0000insulin signaling. Using embedded networks, we discuss evidence of the presence\u0000of a structural \"bifurcation\" in the signaling process between INSMS and INRES.\u0000Concordance profiles of INSMS and INRES show that both have a high propensity\u0000to remain monostationary. Moreover, the concordance properties allow us to\u0000present heuristic evidence that INRES has a higher level of stability beyond\u0000its monostationarity. Finally, we discuss a new way of analyzing reaction\u0000networks through network translation. This method gives rise to three new\u0000insights: (i) each stoichiometric class of INSMS and INRES contains a unique\u0000positive equilibrium; (ii) any positive equilibrium of INSMS is exponentially\u0000stable and is a global attractor in its stoichiometric class; and (iii) any\u0000positive equilibrium of INRES is locally asymptotically stable. These results\u0000open up opportunities for collaboration with experimental biologists to\u0000understand insulin signaling better.","PeriodicalId":501325,"journal":{"name":"arXiv - QuanBio - Molecular Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141153992","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Joshua Pickard, Cooper Stansbury, Amit Surana, Anthony Bloch, Indika Rajapakse
Biomarker selection and real-time monitoring of cell dynamics remains an active challenge in cell biology and biomanufacturing. Here, we develop scalable adaptations of classic approaches to sensor selection for biomarker identification on several transcriptomics and biological datasets that are otherwise cannot be studied from a controls perspective. To address challenges in system identification of biological systems and provide robust biomarkers, we propose Dynamic and Structure Guided Sensors Selection (DSS and SGSS), methods by which temporal models and structural experimental data can be used to supplement traditional approaches to sensor selection. These approaches leverage temporal models and experimental data to enhance traditional sensor selection techniques. Unlike conventional methods that assume well-known, fixed dynamics, DSS and SGSS adaptively select sensors that maximize observability while accounting for the time-varying nature of biological systems. Additionally, they incorporate structural information to identify robust sensors even in cases where system dynamics are poorly understood. We validate these two approaches by performing estimation on several high dimensional systems derived from temporal gene expression data from partial observations.
{"title":"Biomarker Selection for Adaptive Systems","authors":"Joshua Pickard, Cooper Stansbury, Amit Surana, Anthony Bloch, Indika Rajapakse","doi":"arxiv-2405.09809","DOIUrl":"https://doi.org/arxiv-2405.09809","url":null,"abstract":"Biomarker selection and real-time monitoring of cell dynamics remains an\u0000active challenge in cell biology and biomanufacturing. Here, we develop\u0000scalable adaptations of classic approaches to sensor selection for biomarker\u0000identification on several transcriptomics and biological datasets that are\u0000otherwise cannot be studied from a controls perspective. To address challenges\u0000in system identification of biological systems and provide robust biomarkers,\u0000we propose Dynamic and Structure Guided Sensors Selection (DSS and SGSS),\u0000methods by which temporal models and structural experimental data can be used\u0000to supplement traditional approaches to sensor selection. These approaches\u0000leverage temporal models and experimental data to enhance traditional sensor\u0000selection techniques. Unlike conventional methods that assume well-known, fixed\u0000dynamics, DSS and SGSS adaptively select sensors that maximize observability\u0000while accounting for the time-varying nature of biological systems.\u0000Additionally, they incorporate structural information to identify robust\u0000sensors even in cases where system dynamics are poorly understood. We validate\u0000these two approaches by performing estimation on several high dimensional\u0000systems derived from temporal gene expression data from partial observations.","PeriodicalId":501325,"journal":{"name":"arXiv - QuanBio - Molecular Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141060374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lun Ai, Stephen H. Muggleton, Shi-Shun Liang, Geoff S. Baldwin
Techniques to autonomously drive research have been prominent in Computational Scientific Discovery, while Synthetic Biology is a field of science that focuses on designing and constructing new biological systems for useful purposes. Here we seek to apply logic-based machine learning techniques to facilitate cellular engineering and drive biological discovery. Comprehensive databases of metabolic processes called genome-scale metabolic network models (GEMs) are often used to evaluate cellular engineering strategies to optimise target compound production. However, predicted host behaviours are not always correctly described by GEMs, often due to errors in the models. The task of learning the intricate genetic interactions within GEMs presents computational and empirical challenges. To address these, we describe a novel approach called Boolean Matrix Logic Programming (BMLP) by leveraging boolean matrices to evaluate large logic programs. We introduce a new system, $BMLP_{active}$, which efficiently explores the genomic hypothesis space by guiding informative experimentation through active learning. In contrast to sub-symbolic methods, $BMLP_{active}$ encodes a state-of-the-art GEM of a widely accepted bacterial host in an interpretable and logical representation using datalog logic programs. Notably, $BMLP_{active}$ can successfully learn the interaction between a gene pair with fewer training examples than random experimentation, overcoming the increase in experimental design space. $BMLP_{active}$ enables rapid optimisation of metabolic models to reliably engineer biological systems for producing useful compounds. It offers a realistic approach to creating a self-driving lab for microbial engineering.
{"title":"Boolean matrix logic programming for active learning of gene functions in genome-scale metabolic network models","authors":"Lun Ai, Stephen H. Muggleton, Shi-Shun Liang, Geoff S. Baldwin","doi":"arxiv-2405.06724","DOIUrl":"https://doi.org/arxiv-2405.06724","url":null,"abstract":"Techniques to autonomously drive research have been prominent in\u0000Computational Scientific Discovery, while Synthetic Biology is a field of\u0000science that focuses on designing and constructing new biological systems for\u0000useful purposes. Here we seek to apply logic-based machine learning techniques\u0000to facilitate cellular engineering and drive biological discovery.\u0000Comprehensive databases of metabolic processes called genome-scale metabolic\u0000network models (GEMs) are often used to evaluate cellular engineering\u0000strategies to optimise target compound production. However, predicted host\u0000behaviours are not always correctly described by GEMs, often due to errors in\u0000the models. The task of learning the intricate genetic interactions within GEMs\u0000presents computational and empirical challenges. To address these, we describe\u0000a novel approach called Boolean Matrix Logic Programming (BMLP) by leveraging\u0000boolean matrices to evaluate large logic programs. We introduce a new system,\u0000$BMLP_{active}$, which efficiently explores the genomic hypothesis space by\u0000guiding informative experimentation through active learning. In contrast to\u0000sub-symbolic methods, $BMLP_{active}$ encodes a state-of-the-art GEM of a\u0000widely accepted bacterial host in an interpretable and logical representation\u0000using datalog logic programs. Notably, $BMLP_{active}$ can successfully learn\u0000the interaction between a gene pair with fewer training examples than random\u0000experimentation, overcoming the increase in experimental design space.\u0000$BMLP_{active}$ enables rapid optimisation of metabolic models to reliably\u0000engineer biological systems for producing useful compounds. It offers a\u0000realistic approach to creating a self-driving lab for microbial engineering.","PeriodicalId":501325,"journal":{"name":"arXiv - QuanBio - Molecular Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140931073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kristoffer R. Thomsen, Artemy Kolchinsky, Steen Rasmussen
Critical experimental design issues connecting energy transduction and inheritable information within a protocell are explored and elucidated. The protocell design utilizes a photo-driven energy transducer (a ruthenium complex) to turn resource molecules into building blocks, in a manner that is modulated by a combinatorial DNA-based co-factor. This co-factor molecule serves as part of an electron relay for the energy transduction mechanism, where the charge-transport rates depend on the sequence that contains an oxo-guanine. The co-factor also acts as a store of inheritable information due to its ability to replicate non-enzymatically through template-directed ligation. Together, the energy transducer and the co-factor act as a metabolic catalyst that produces co-factor DNA building blocks as well as fatty acids (from picolinium ester and modified DNA oligomers), where the fatty acids self-assemble into vesicles on which exterior surface both the co-factor (DNA) and the energy transducer are anchored with hydrophobic tails. Here we use simulations to study how the co-factor sequence determines its fitness as reflected by charge transfer and replication rates. To estimate the impact on the protocell, we compare these rates with previously measured metabolic rates from a similar system where the charge transfer is directly between the ruthenium complex and the oxo-guanine (without DNA replication and charge transport). Replication and charge transport turn out to have different and often opposing sequence requirements. Functional information of the co-factor molecules is used to probe the feasibility of randomly picking co-factor sequences from a limited population of co-factors molecules, where a good co-factor can enhance both metabolic biomass production and its own replication rate.
本研究探讨并阐明了原电池内连接能量转换和可遗传信息的关键实验设计问题。原电池的设计利用光驱动能量转换器（钌复合物）将资源分子转化为构件，转化方式由基于 DNA 的组合辅助因子调节。这种辅助因子分子是能量转换机制电子中继的一部分，其中电荷转移速率取决于含有缺氧鸟嘌呤的序列。该辅助因子还可以通过模板定向连接进行非酶促复制，从而起到储存可遗传信息的作用。能量转换器和辅助因子共同充当新陈代谢催化剂，产生辅助因子 DNA 构建块以及脂肪酸（来自吡啶甲酸酯和修饰的 DNA 寡聚体）。在这里，我们通过模拟来研究辅助因子序列如何通过电荷转移和复制率来决定其适应性。为了估算对原电池的影响，我们将这些速率与之前从类似系统中测得的代谢速率进行了比较，在该系统中，电荷转移直接发生在钌复合物和氧鸟嘌呤之间（没有 DNA 复制和电荷转移）。结果表明，复制和电荷转移有不同的序列要求，而且往往是相反的。辅助因子分子的功能信息被用来探究从有限的辅助因子分子群中随机挑选辅助因子序列的可行性。
{"title":"Metabolism, information, and viability in a simulated physically-plausible protocell","authors":"Kristoffer R. Thomsen, Artemy Kolchinsky, Steen Rasmussen","doi":"arxiv-2405.04654","DOIUrl":"https://doi.org/arxiv-2405.04654","url":null,"abstract":"Critical experimental design issues connecting energy transduction and\u0000inheritable information within a protocell are explored and elucidated. The\u0000protocell design utilizes a photo-driven energy transducer (a ruthenium\u0000complex) to turn resource molecules into building blocks, in a manner that is\u0000modulated by a combinatorial DNA-based co-factor. This co-factor molecule\u0000serves as part of an electron relay for the energy transduction mechanism,\u0000where the charge-transport rates depend on the sequence that contains an\u0000oxo-guanine. The co-factor also acts as a store of inheritable information due\u0000to its ability to replicate non-enzymatically through template-directed\u0000ligation. Together, the energy transducer and the co-factor act as a metabolic\u0000catalyst that produces co-factor DNA building blocks as well as fatty acids\u0000(from picolinium ester and modified DNA oligomers), where the fatty acids\u0000self-assemble into vesicles on which exterior surface both the co-factor (DNA)\u0000and the energy transducer are anchored with hydrophobic tails. Here we use\u0000simulations to study how the co-factor sequence determines its fitness as\u0000reflected by charge transfer and replication rates. To estimate the impact on\u0000the protocell, we compare these rates with previously measured metabolic rates\u0000from a similar system where the charge transfer is directly between the\u0000ruthenium complex and the oxo-guanine (without DNA replication and charge\u0000transport). Replication and charge transport turn out to have different and\u0000often opposing sequence requirements. Functional information of the co-factor\u0000molecules is used to probe the feasibility of randomly picking co-factor\u0000sequences from a limited population of co-factors molecules, where a good\u0000co-factor can enhance both metabolic biomass production and its own replication\u0000rate.","PeriodicalId":501325,"journal":{"name":"arXiv - QuanBio - Molecular Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140942022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fernando Antoneli, Martin Golubitsky, Jiaxin Jin, Ian Stewart
Homeostasis is concerned with regulatory mechanisms, present in biological systems, where some specific variable is kept close to a set value as some external disturbance affects the system. Mathematically, the notion of homeostasis can be formalized in terms of an input-output function that maps the parameter representing the external disturbance to the output variable that must be kept within a fairly narrow range. This observation inspired the introduction of the notion of infinitesimal homeostasis, namely, the derivative of the input-output function is zero at an isolated point. This point of view allows for the application of methods from singularity theory to characterize infinitesimal homeostasis points (i.e. critical points of the input-output function). In this paper we review the infinitesimal approach to the study of homeostasis in input-output networks. An input-output network is a network with two distinguished nodes `input' and `output', and the dynamics of the network determines the corresponding input-output function of the system. This class of dynamical systems provides an appropriate framework to study homeostasis and several important biological systems can be formulated in this context. Moreover, this approach, coupled to graph-theoretic ideas from combinatorial matrix theory, provides a systematic way for classifying different types of homeostasis (homeostatic mechanisms) in input-output networks, in terms of the network topology. In turn, this leads to new mathematical concepts, such as, homeostasis subnetworks, homeostasis patterns, homeostasis mode interaction. We illustrate the usefulness of this theory with several biological examples: biochemical networks, chemical reaction networks (CRN), gene regulatory networks (GRN), Intracellular metal ion regulation and so on.
{"title":"Homeostasis in Input-Output Networks: Structure, Classification and Applications","authors":"Fernando Antoneli, Martin Golubitsky, Jiaxin Jin, Ian Stewart","doi":"arxiv-2405.03861","DOIUrl":"https://doi.org/arxiv-2405.03861","url":null,"abstract":"Homeostasis is concerned with regulatory mechanisms, present in biological\u0000systems, where some specific variable is kept close to a set value as some\u0000external disturbance affects the system. Mathematically, the notion of\u0000homeostasis can be formalized in terms of an input-output function that maps\u0000the parameter representing the external disturbance to the output variable that\u0000must be kept within a fairly narrow range. This observation inspired the\u0000introduction of the notion of infinitesimal homeostasis, namely, the derivative\u0000of the input-output function is zero at an isolated point. This point of view\u0000allows for the application of methods from singularity theory to characterize\u0000infinitesimal homeostasis points (i.e. critical points of the input-output\u0000function). In this paper we review the infinitesimal approach to the study of\u0000homeostasis in input-output networks. An input-output network is a network with\u0000two distinguished nodes `input' and `output', and the dynamics of the network\u0000determines the corresponding input-output function of the system. This class of\u0000dynamical systems provides an appropriate framework to study homeostasis and\u0000several important biological systems can be formulated in this context.\u0000Moreover, this approach, coupled to graph-theoretic ideas from combinatorial\u0000matrix theory, provides a systematic way for classifying different types of\u0000homeostasis (homeostatic mechanisms) in input-output networks, in terms of the\u0000network topology. In turn, this leads to new mathematical concepts, such as,\u0000homeostasis subnetworks, homeostasis patterns, homeostasis mode interaction. We\u0000illustrate the usefulness of this theory with several biological examples:\u0000biochemical networks, chemical reaction networks (CRN), gene regulatory\u0000networks (GRN), Intracellular metal ion regulation and so on.","PeriodicalId":501325,"journal":{"name":"arXiv - QuanBio - Molecular Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140931023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Gene duplication is a fundamental evolutionary mechanism that contributes to biological complexity and diversity (Fortna et al., 2004). Traditionally, research has focused on the duplication of gene sequences (Zhang, 1914). However, evidence suggests that the duplication of regulatory elements may also play a significant role in the evolution of genomic functions (Teichmann and Babu, 2004; Hallin and Landry, 2019). In this work, the evolution of regulatory relationships belonging to gene-specific-substructures in a GRN are modeled. In the model, a network grows from an initial configuration by repeatedly choosing a random gene to duplicate. The likelihood that the regulatory relationships associated with the selected gene are retained through duplication is determined by a vector of probabilities. Occurrences of gene-family-specific substructures are counted under the gene duplication model. In this thesis, gene-family-specific substructures are referred to as subnetwork motifs. These subnetwork motifs are motivated by network motifs which are patterns of interconnections that recur more often in a specialized network than in a random network (Milo et al., 2002). Subnetwork motifs differ from network motifs in the way that subnetwork motifs are instances of gene-family-specific substructures while network motifs are isomorphic substructures. These subnetwork motifs are counted under Full and Partial Duplication, which differ in the way in which regulation relationships are inherited. Full duplication occurs when all regulatory links are inherited at each duplication step, and Partial Duplication occurs when regulation inheritance varies at each duplication step. Moments for the number of occurrences of subnetwork motifs are determined in each model. The results presented offer a method for discovering subnetwork motifs that are significant in a GRN under gene duplication.
基因复制是一种基本的进化机制，有助于提高生物学的复杂性和多样性（Fortna 等人，2004 年）。然而，有证据表明，调控元件的复制也可能在基因组功能的进化中发挥重要作用（Teichmann andBabu, 2004; Hallin and Landry, 2019）。在这项工作中，我们模拟了基因组网络中属于基因特异性子结构的调控关系的进化。在该模型中，通过重复选择随机基因进行复制，网络从初始配置开始生长。与所选基因相关的调控关系通过复制得以保留的可能性由概率向量决定。在基因复制模型下，基因家族特异性子结构的出现率被计算在内。在本论文中，基因家族特异的子结构被称为子网络主题（subnetwork motifs）。子网络动机是受网络动机（network motifs）的启发而产生的，网络动机是指在专门网络中比在随机网络中更经常出现的互连模式（Milo et al.子网络图案与网络图案的不同之处在于，子网络图案是基因家族特定子结构的实例，而网络图案是同构的子结构。这些子网络基元被归入完全复制和部分复制范畴，两者在调控关系的遗传方式上有所不同。完全复制发生在每个复制步骤都继承所有调控联系的情况下，而部分复制发生在每个复制步骤的调控继承都不同的情况下。在每个模型中都确定了子网络图案出现次数的矩。研究结果提供了一种方法，用于发现在基因重复情况下对 GRN 有重要意义的子网络主题。
{"title":"Counting Subnetworks Under Gene Duplication in Genetic Regulatory Networks","authors":"Ashley Scruse, Jonathan Arnold, Robert Robinson","doi":"arxiv-2405.03148","DOIUrl":"https://doi.org/arxiv-2405.03148","url":null,"abstract":"Gene duplication is a fundamental evolutionary mechanism that contributes to\u0000biological complexity and diversity (Fortna et al., 2004). Traditionally,\u0000research has focused on the duplication of gene sequences (Zhang, 1914).\u0000However, evidence suggests that the duplication of regulatory elements may also\u0000play a significant role in the evolution of genomic functions (Teichmann and\u0000Babu, 2004; Hallin and Landry, 2019). In this work, the evolution of regulatory\u0000relationships belonging to gene-specific-substructures in a GRN are modeled. In\u0000the model, a network grows from an initial configuration by repeatedly choosing\u0000a random gene to duplicate. The likelihood that the regulatory relationships\u0000associated with the selected gene are retained through duplication is\u0000determined by a vector of probabilities. Occurrences of gene-family-specific\u0000substructures are counted under the gene duplication model. In this thesis,\u0000gene-family-specific substructures are referred to as subnetwork motifs. These\u0000subnetwork motifs are motivated by network motifs which are patterns of\u0000interconnections that recur more often in a specialized network than in a\u0000random network (Milo et al., 2002). Subnetwork motifs differ from network\u0000motifs in the way that subnetwork motifs are instances of gene-family-specific\u0000substructures while network motifs are isomorphic substructures. These\u0000subnetwork motifs are counted under Full and Partial Duplication, which differ\u0000in the way in which regulation relationships are inherited. Full duplication\u0000occurs when all regulatory links are inherited at each duplication step, and\u0000Partial Duplication occurs when regulation inheritance varies at each\u0000duplication step. Moments for the number of occurrences of subnetwork motifs\u0000are determined in each model. The results presented offer a method for\u0000discovering subnetwork motifs that are significant in a GRN under gene\u0000duplication.","PeriodicalId":501325,"journal":{"name":"arXiv - QuanBio - Molecular Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140885402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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